The Quest for New Drugs

In Plattform Life Sciences, a German life sciences journal, Anton Dolgikh, Head of AI at DataArt, discusses the role of artificial intelligence in drug discovery, namely its significant impact on targets search, hits/leads search, adverse effects reduction, and old drugs repositioning.

"The drug development process that starts with a simple molecule and ends up with a novel drug takes from 12 to 15 years and costs more than one billion dollars. Moreover, the probability for a new drug to be approved by FDA is only 8% 3. Thus, it is not surprising that much effort is directed at shortening the total time of drug development and reducing the cost. One certain route to achieving these goals lies in utilizing the computational and predictive power of Artificial Intelligence (AI).

A search for new drugs is initiated by identifying the targets. … Information on the targets can be obtained experimentally by high-throughput screening, or new targets can be found in scientific publications... If we consider that the number of publications in PubMed database is more than 18 million abstracts and every month about 60000 new abstracts are added(Yang, Adelstein and Kassis, 2009), it is scarcely possible for a human researcher to read so many articles in their whole life. At the same time, AI augmented data mining enables the extraction of information about novel targets from publicly available sources: publications, patents, and other sources by using Natural Language Processing methods. As reported by BenevolentBio’s CEO Jackie Hunter, it is possible to gain a fourfold increase in speed of identifying targets, validation, and the R&D success rate with the adoption of deep learning(Ernst & Young LLP, 2017).

AI can efficiently filter out all “non-interesting” molecules from the list. An impressive example of such filtering is a deep neural network built by the Insilico Medicine start-up, where 69 molecules with a specific anti-cancer characteristics were identified from 72 million entities stored in PubChem (Kadurin et al., 2016).

AI methods, e.g. automated QSAR or MMPA, are used to run chemical predictive modeling to foresee the adverse effects and assist a process of chemical optimization of the leads. QSAR models apply regression and classification approaches to find the meaningfulpatterning in chemical databases."